Detalhes bibliográficos
| Ano de defesa: |
2024 |
| Autor(a) principal: |
Soares, Daniel de Almeida
 |
| Orientador(a): |
Chaves, Andréa Rodrigues
|
| Banca de defesa: |
Chaves, Andréa Rodrigues,
Rezende, Cintia Silva Minafra e,
Lima, Leomir Aires Silva de,
Alves, Maria Isabel Ribeiro,
Brito, Nubia Natalia De |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Goiás
|
| Programa de Pós-Graduação: |
Programa de Pós-graduação em Química (IQ)
|
| Departamento: |
Instituto de Química - IQ (RMG)
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://repositorio.bc.ufg.br/tede/handle/tede/13718
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Resumo: |
The present study uses liquid chromatography and mass spectrometry approaches as tools for identifying and quantifying contaminants in foods, such as rice and bovine milk. For the sake of comprehension, this study is divided into four stages: 1) Development of an LC-MS methodology for quantifying the antibiotic ceftiofur in bovine milk samples; 2) Determination of bisphenols (A and S) in long-life milk and its respective packaging by Paper Spray Ionization (PSI-MS); 3) Development of a methodology for determining herbicides in rice (Oryza sativa) samples using the miniaturized hollow fiber liquid phase microextraction sample preparation technique (HF-LPME) with electrospray ionization analysis (ESI-MS); 4) Chemometric analysis comparing rice samples contaminated with fungicides and not contaminated from direct analysis by ESI-MS. In step 1, to determine ceftiofur in milk samples, a liquidliquid extraction (ELL) method was developed followed by analysis of the extracts by LC-MS. The main variables associated with the extraction process were optimized and the figures of merit: linearity, limit of detection and quantification (LOD and LOQ), precision and recovery were evaluated. The method presented LOQ 0.80 ng.mL-1 , inter-day precision of less than 7.3% and recovery greater than 94%. Milk samples obtained from cows subjected to antibiotic application were analyzed for the purpose of evaluating the antibiotic elimination profile in the milk samples. In part 2, it was possible to develop a rapid method for analyzing bisphenols (BPA and BPS) in longlife milk samples and their respective packaging using ambient mass spectrometry using the PSI-MS technique. The method was optimized and the figures of merit linearity, precision, accuracy, LOQ and LOD were evaluated, all of which were within the parameters required by national legislation. The bisphenols under study were detected in all samples (packaging and milk) and in only one sample the BPA content was above 10 ng.mL-1 (maximum residue limit) and for BPS the majority of samples presented levels above the same concentration. In milk, a content of up to 150 ng.mL1 for BPA and an average content of 60 ng.mL-1 for BPS was obtained. The results of this study were published in the journal Food Chemistry (qualis A1). In chapter 3, the use of the miniaturized HF-LPME sample preparation technique followed by ESI ionization mass spectrometry analysis for the determination of herbicides in rice is presented. The main parameters relating to the sample preparation method were optimized, with the use of a two-phase system resulting in shorter extraction times. However, in a three-phase system, with the donor phase at pH 1.7 and the acceptor phase at pH 12, extraction was favored for all analytes. The ionic strength was evaluated by adding salt to the donor solution and did not favor extraction. Therefore, the three-phase system presented a better response to the analytes and the method should have its figures of merit evaluated using the optimized HF-LPME/ESI-MS method. The fourth chapter presents a metabolomic analysis followed by chemometric treatment of data obtained by ESI-MS analysis on rice extract samples using positive and negative modes. A comparison of samples was carried out in 4 different matrices (rice husk, grain with husk, polished grain without husk and unpolished grain without husk). The samples evaluated were divided into two groups: contaminated with fungicides and not contaminated. The data obtained made it possible to separate them into classes using treatments using principal component analysis (PCA). In ESI(-) mode, the data were better explained by PCA Without normalization (89.1%) and also by Quantile Normalization (90%). The negative mode better explains the dissimilarity between the contaminated and uncontaminated rice groups. |
